(1)H NMR metabolomics identification of markers of hypoxia-induced metabolic shifts in a breast cancer model system

Targeted Metabolomics in High Performance Sports: Differences between the Resting Metabolic Profile of Endurance- and Strength-Trained Athletes in Comparison with Sedentary Subjects over the Course of a Training Year

Little is known about the metabolic differences between endurance and strength athletes in comparison with sedentary subjects under controlled conditions and about variation of the metabolome throughout one year. We hypothesized that (1) the resting metabolic profile differs between sedentary subjects and athletes and between perennially endurance- and strength-trained athletes and (2) varies throughout one year of training. We performed quantitative, targeted metabolomics (Biocrates MxP^® Quant 500, Biocrates Life Sciences AG, Innsbruck, Austria) in plasma samples at rest in three groups of male adults, 12 strength-trained (weightlifters, 20 ± 3 years), 10 endurance-trained athletes (runners, 24 ± 3 years), and 12 sedentary subjects (25 ± 4 years) at the end of three training phases (regeneration, preparation, and competition) within one training year. Performance and anthropometric data showed significant (p < 0.05) differences between the groups. Metabolomic analysis revealed different resting metabolic profiles between the groups with acetylcarnitines, di- and triacylglycerols, and glycerophospho- and sphingolipids, as well as several amino acids as the most robust metabolites. Furthermore, we observed changes in free carnitine and 3-methylhistidine in strength-trained athletes throughout the training year. Regular endurance or strength training induces changes in the concentration of several metabolites associated with adaptations of the mitochondrial energy and glycolytic metabolism with concomitant changes in amino acid metabolism and cell signaling.

Feasibility of Acupuncture and Exploration of Metabolomic Alterations for Psychoneurological Symptoms Among Breast Cancer Survivors

OBJECTIVE

Approximately 24-68% of breast cancer survivors report co-occurring psychoneurological symptoms of pain, fatigue, sleep disturbance, depression, and anxiety during and after cancer treatment. This study aimed to assess the feasibility and acceptability of acupuncture for the treatment of multiple psychoneurological symptoms among breast cancer survivors and explore metabolomic changes before and after acupuncture.

METHODS

We conducted a single-arm, prospective pilot study of breast cancer survivors with at least two moderate to severe psychoneurological symptoms (>3 on a 0-10 scale). Acupuncture was administered twice weekly for 5 weeks, for 30 minutes per session. Along with Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaires, a fasting serum comprehensive hydrophilic metabolites panel was analyzed at baseline and after acupuncture.

RESULTS

Eight participants (mean age 52.5 ± 10.9 years; 62.5% Black) were enrolled. Feasibility was supported, with 67% recruitment, 87.5% retention, and 98% acceptability. Post intervention, PROMIS T-scores were reduced for all psychoneurological symptoms. Significant differences in serum metabolites before and after acupuncture were F-1,6/2,6-DP, glutathione disulfide, phosphorylcholine, 6-methylnicotinamide, glutathione, and putrescine (variable importance of projection values larger than 1.5 and p values <0.05). Pathway analysis indicated that glutathione metabolism (p = 0.002, q = 0.071), and arginine and proline metabolisms (p = 0.009, q = 0.166) were potentially involved in mechanisms of acupuncture.

CONCLUSIONS

Acupuncture to reduce multiple psychoneurological symptoms among breast cancer survivors was feasible and acceptable. Study findings also shed light on the metabolic pathways involved in the acupuncture response and will be tested in future studies.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for &gt;40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Ultra-Performance Liquid Chromatography-Mass Spectrometry-Based Untargeted Metabolomics Reveals the Key Potential Biomarkers for Castor Meal-Induced Enteritis in Juvenile Hybrid Grouper (Epinephelus fuscoguttatus♀ × E. lanceolatus♂)

The intensification of aquaculture to help kerb global food security issues has led to the quest for more economical new protein-rich ingredients for the feed-based aquaculture since fishmeal (FM, the ingredient with the finest protein and lipid profile) is losing its acceptability due to high cost and demand. Although very high in protein, castor meal (CM), a by-product after oil-extraction, is disposed-off due to the high presence of toxins. Concurrently, the agro-industrial wastes’ consistent production and disposal are of utmost concern; however, having better nutritional profiles of these wastes can lead to their adoption. This study was conducted to identify potential biomarkers of CM-induced enteritis in juvenile hybrid-grouper (Epinephelus fuscoguttatus♀ × Epinephelus lanceolatus♂) using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) alongside their growth and distal intestinal (DI) health evaluation. A total of 360 fish (initial weight = 9.13 ± 0.01g) were randomly assigned into three groups, namely, fish-meal (FM) (control), 4% CM (CM4), and 20% CM (CM20). After the 56-days feeding-trial, the DI tissues of FM, CM4, and CM20 groups were collected for metabolomics analysis. Principal components analysis and partial least-squares discriminant-analysis (PLS-DA, used to differentiate the CM20 and CM4, from the FM group with satisfactory explanation and predictive ability) were used to analyze the UPLC-MS data. The results revealed a significant improvement in the growth, DI immune responses and digestive enzyme activities, and DI histological examinations in the CM4 group than the others. Nonetheless, CM20 replacement caused DI physiological damage and enteritis in grouper as shown by AB-PAS staining and scanning electron microscopy examinations, respectively. The most influential metabolites in DI contents identified as the potential biomarkers in the positive and negative modes using the metabolomics UPLC-MS profiles were 28 which included five organoheterocyclic compounds, seven lipids, and lipid-like molecules, seven organic oxygen compounds, two benzenoids, five organic acids and derivatives, one phenylpropanoids and polyketides, and one from nucleosides, nucleotides, and analogues superclass. The present study identified a broad array of DI tissue metabolites that differed between FM and CM diets, which provides a valuable reference for further managing fish intestinal health issues. A replacement level of 4% is recommended based on the growth and immunity of fish.

Impact of the Pd2Spm (Spermine) Complex on the Metabolism of Triple-Negative Breast Cancer Tumors of a Xenograft Mouse Model

The interest in palladium(II) compounds as potential new anticancer drugs has increased in recent years, due to their high toxicity and acquired resistance to platinum(II)-derived agents, namely cisplatin. In fact, palladium complexes with biogenic polyamines (e.g., spermine, Pd₂Spm) have been known to display favorable antineoplastic properties against distinct human breast cancer cell lines. This study describes the in vivo response of triple-negative breast cancer (TNBC) tumors to the Pd₂Spm complex or to cisplatin (reference drug), compared to tumors in vehicle-treated mice. Both polar and lipophilic extracts of tumors, excised from a MDA-MB-231 cell-derived xenograft mouse model, were characterized through nuclear magnetic resonance (NMR) metabolomics. Interestingly, the results show that polar and lipophilic metabolomes clearly exhibit distinct responses for each drug, with polar metabolites showing a stronger impact of the Pd(II)-complex compared to cisplatin, whereas neither drug was observed to significantly affect tumor lipophilic metabolism. Compared to cisplatin, exposure to Pd₂Spm triggered a higher number of, and more marked, variations in some amino acids, nucleotides and derivatives, membrane precursors (choline and phosphoethanolamine), dimethylamine, fumarate and guanidine acetate, a signature that may be relatable to the cytotoxicity and/or mechanism of action of the palladium complex. Putative explanatory biochemical hypotheses are advanced on the role of the new Pd₂Spm complex in TNBC metabolism.

Metabolomics and transcriptomics to decipher molecular mechanisms underlying ectomycorrhizal root colonization of an oak tree

Mycorrhizas are known to have a positive impact on plant growth and ability to resist major biotic and abiotic stresses. However, the metabolic alterations underlying mycorrhizal symbiosis are still understudied. By using metabolomics and transcriptomics approaches, cork oak roots colonized by the ectomycorrhizal fungus Pisolithus tinctorius were compared with non-colonized roots. Results show that compounds putatively corresponding to carbohydrates, organic acids, tannins, long-chain fatty acids and monoacylglycerols, were depleted in ectomycorrhizal cork oak colonized roots. Conversely, non-proteogenic amino acids, such as gamma-aminobutyric acid (GABA), and several putative defense-related compounds, including oxylipin-family compounds, terpenoids and B6 vitamers were induced in mycorrhizal roots. Transcriptomic analysis suggests the involvement of GABA in ectomycorrhizal symbiosis through increased synthesis and inhibition of degradation in mycorrhizal roots. Results from this global metabolomics analysis suggest decreases in root metabolites which are common components of exudates, and in compounds related to root external protective layers which could facilitate plant-fungal contact and enhance symbiosis. Root metabolic pathways involved in defense against stress were induced in ectomycorrhizal roots that could be involved in a plant mechanism to avoid uncontrolled growth of the fungal symbiont in the root apoplast. Several of the identified symbiosis-specific metabolites, such as GABA, may help to understand how ectomycorrhizal fungi such as P. tinctorius benefit their host plants.

The effect of acute moderate-intensity exercise on the serum and fecal metabolomes and the gut microbiota of cross-country endurance athletes

Physical exercise can produce changes in the microbiota, conferring health benefits through mechanisms that are not fully understood. We sought to determine the changes driven by exercise on the gut microbiota and on the serum and fecal metabolome using 16S rRNA gene analysis and untargeted metabolomics. A total of 85 serum and 12 fecal metabolites and six bacterial taxa (Romboutsia, Escherichia coli TOP498, Ruminococcaceae UCG-005, Blautia, Ruminiclostridium 9 and Clostridium phoceensis) were modified following a controlled acute exercise session. Among the bacterial taxa, Ruminiclostridium 9 was the most influenced by fecal and serum metabolites, as revealed by linear multivariate regression analysis. Exercise significantly increased the fecal ammonia content. Functional analysis revealed that alanine, aspartate and glutamate metabolism and the arginine and aminoacyl-tRNA biosynthesis pathways were the most relevant modified pathways in serum, whereas the phenylalanine, tyrosine and tryptophan biosynthesis pathway was the most relevant pathway modified in feces. Correlation analysis between fecal and serum metabolites suggested an exchange of metabolites between both compartments. Thus, the performance of a single exercise bout in cross-country non-professional athletes produces significant changes in the microbiota and in the serum and fecal metabolome, which may have health implications.

Metabolomic studies of breast cancer in murine models: A review

BACKGROUND

Metabolomic strategies have been extensively used to search for biomarkers of disease, including cancer, in biological complex mixtures such as cells, tissues and biofluids. In breast cancer research, murine models are of great value and metabolomics has been increasingly applied to characterize tumor or organ tissues, or biofluids, for instance to follow-up metabolism during cancer progression or response to specific therapies.

SCOPE OF REVIEW

This review briefly introduces the different murine models used in breast cancer research and proceeds to present the metabolomic studies reported so far to describe the deviant metabolic behavior associated to breast cancer, in each type of model: xenografts (cell- or patient-derived), spontaneous (naturally-occurring or genetically engineered) and carcinogen-induced. The type of sample and strategies followed are identified, as well as the main findings from of study.

MAJOR CONCLUSIONS

Metabolomics has gradually become relevant in characterizing murine models of breast cancer, using either Nuclear Magnetic Resonance (NMR) or Mass Spectromety (MS). Both tissue and biofluids are matrixes of interest in this context, although in some type of models, reports have focused primarily on the former. The aims of tissue studies have comprised the search for mechanistic knowledge of carcinogenesis, metastasis development and response/resistance to therapies. Biofluid metabolomics has mainly aimed at finding non-invasive biomarkers for early breast cancer detection or prognosis determination.

GENERAL SIGNIFICANCE

Metabolomics provides exquisite detail on murine tumor and systemic metabolism of breast cancer. This knowledge paves the way for the discovery of new biomarkers, potentially translatable to in vivo non-invasive patient follow-up.

Hair Metabolomics in Animal Studies and Clinical Settings

Metabolomics is a powerful tool used to understand comprehensive changes in the metabolic response and to study the phenotype of an organism by instrumental analysis. It most commonly involves mass spectrometry followed by data mining and metabolite assignment. For the last few decades, hair has been used as a valuable analytical sample to investigate retrospective xenobiotic exposure as it provides a wider window of detection than other biological samples such as saliva, plasma, and urine. Hair contains functional metabolomes such as amino acids and lipids. Moreover, segmental analysis of hair based on its growth rate can provide information on metabolic changes over time. Therefore, it has great potential as a metabolomics sample to monitor chronic diseases, including drug addiction or abnormal conditions. In the current review, the latest applications of hair metabolomics in animal studies and clinical settings are highlighted. For this purpose, we review and discuss the characteristics of hair as a metabolomics sample, the analytical techniques employed in hair metabolomics and the consequence of hair metabolome alterations in recent studies. Through this, the value of hair as an alternative biological sample in metabolomics is highlighted.

Current Status and Future Prospects of Clinically Exploiting Cancer-specific Metabolism-Why Is Tumor Metabolism Not More Extensively Translated into Clinical Targets and Biomarkers?

Tumor cells exhibit a specialized metabolism supporting their superior ability for rapid proliferation, migration, and apoptotic evasion. It is reasonable to assume that the specific metabolic needs of the tumor cells can offer an array of therapeutic windows as pharmacological disturbance may derail the biochemical mechanisms necessary for maintaining the tumor characteristics, while being less important for normally proliferating cells. In addition, the specialized metabolism may leave a unique metabolic signature which could be used clinically for diagnostic or prognostic purposes. Quantitative global metabolic profiling (metabolomics) has evolved over the last two decades. However, despite the technology’s present ability to measure 1000s of endogenous metabolites in various clinical or biological specimens, there are essentially no examples of metabolomics investigations being translated into actual utility in the cancer clinic. This review investigates the current efforts of using metabolomics as a tool for translation of tumor metabolism into the clinic and further seeks to outline paths for increasing the momentum of using tumor metabolism as a biomarker and drug target opportunity.

Rapid, Reproducible, Quantifiable NMR Metabolomics: Methanol and Methanol: Chloroform Precipitation for Removal of Macromolecules in Serum and Whole Blood

Background: Though blood is an excellent biofluid for metabolomics, proteins and lipids present in blood can interfere with 1d-¹H NMR spectra and disrupt quantification of metabolites. Here, we present effective macromolecule removal strategies for serum and whole blood (WB) samples. Methods: A variety of macromolecule removal strategies were compared in both WB and serum, along with tests of ultrafiltration alone and in combination with precipitation methods. Results: In healthy human serum, methanol:chloroform:water extraction with ultrafiltration was compared to methanol precipitation with and without ultrafiltration. Methods were tested in healthy pooled human serum, and in serum from patients with sepsis. Effects of long-term storage at −80 °C were tested to explore the impact of macromolecule removal strategy on serum from different conditions. In WB a variety of extraction strategies were tested in two types of WB (from pigs and baboons) to examine the impact of macromolecule removal strategies on different samples. Conclusions: In healthy human serum methanol precipitation of serum with ultrafiltration was superior, but was similar in recovery and variance to methanol:chloroform:water extraction with ultrafiltration in pooled serum from patients with sepsis. In WB, high quality, quantifiable spectra were obtained with the use of a methanol: chloroform precipitation.

Metabolic fingerprinting in breast cancer stages through 1H NMR spectroscopy-based metabolomic analysis of plasma

Breast cancer (BC) is one of the most common malignancies among women worldwide, which is indeed associated with metabolic reprogramming. However, BC is a very complex and heterogeneous disease, which can relate with the changes in metabolic profiles during BC progression. Hence, investigating the metabolic alterations during BC stage progression may reveal the deregulated pathways and useful metabolic signatures of BC. To demonstrate the metabolic insights, we opted ¹H NMR spectroscopy based metabolomics of blood plasma of early and late stage BC (N = 72) with age and gender matched healthy subjects (N = 50). Further, the metabolic profiles were analyzed to delineate the potential signatures of BC by performing multivariate and nonparametric statistical analysis in early and late stages of BC in comparison with healthy subjects. Sixteen metabolites levels were differentially changed (p < 0.05) in the early and late stages of BC from healthy subjects. Among them, the levels of hydroxybutyrate, lysine, glutamate, glucose, N-acetyl glycoprotein, Lactate were highly distinguished in BC stages and showed a good biomarker potential using receiver-operating curves based diagnostic models. Furthermore, the significant modulation and good diagnostic performances of glutamate, N-acetyl glycoprotein and Lactate in LBC as compared to EBC give their significance in the BC progression. In general, our observations demonstrate that these panels of metabolites may act as vital component of the metabolism of early to late stage BC progression. Our results also open new avenue towards early and late stage BC diagnosis and intervention implying metabolomics approaches.

Untargeted and stable isotope-assisted metabolomic analysis of MDA-MB-231 cells under hypoxia

INTRODUCTION

Hypoxia commonly occurs in cancers and is highly related with the occurrence, development and metastasis of cancer. Treatment of triple negative breast cancer remains challenge. Knowledge about the metabolic status of triple negative breast cancer cell lines in hypoxia is valuable for the understanding of molecular mechanisms of this tumor subtype to develop effective therapeutics.

OBJECTIVES

Comprehensively characterize the metabolic profiles of triple negative breast cancer cell line MDA-MB-231 in normoxia and hypoxia and the pathways involved in metabolic changes in hypoxia.

METHODS

Differences in metabolic profiles affected pathways of MDA-MB-231 cells in normoxia and hypoxia were characterized using GC-MS based untargeted and stable isotope assisted metabolomic techniques.

RESULTS

Thirty-three metabolites were significantly changed in hypoxia and nine pathways were involved. Hypoxia increased glycolysis, inhibited TCA cycle, pentose phosphate pathway and pyruvate carboxylation, while increased glutaminolysis in MDA-MB-231 cells.

CONCLUSION

The current results provide metabolic differences of MDA-MB-231 cells in normoxia and hypoxia conditions as well as the involved metabolic pathways, demonstrating the power of combined use of untargeted and stable isotope-assisted metabolomic methods in comprehensive metabolomic analysis.

Metabonomics analysis of serum from rats given long-term and low-level cadmium by ultra-performance liquid chromatography-mass spectrometry

1. This study evaluated the toxicity of chronic exposure to low-level cadmium (Cd) in rats using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Forty male Sprague-Dawley rats were randomly assigned to four groups, namely, the control group, low-dose group (0.13 mg/kg·bw), middle-dose group (0.8 mg/kg·bw) and high-dose group (4.89 mg/kg·bw). The rats continuously received CdCl₂ via drinking water for 24 weeks. Serum samples were collected for metabonomics analysis. The data generated from the UPLC-MS was analysed using principal components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). PLS-DA model with satisfactory explanatory and predictive ability is capable of discriminating the treatment groups from the control group. 2. Finally, the 10 metabolites were identified and showed significant changes in some treatment groups compared with that in the control group (p < 0.0167 or p < 0.003). Exposure to Cd resulted in increased intensities of lysophosphatidic acid (P-16:0e/0:0), glycocholic acid, bicyclo-prostaglandin E2, lithocholyltaurine, sulfolithocholylglycine, lysophosphatidylethanolamine (20:5/0:0) and lysophosphatidylcholine (20:0), as well as decreased intensities of 3-indolepropionic acid, phosphatidylcholine (18:4/18:0) and 15S-hydroxyeicosatrienoic acid in rat serum. 3. Results suggest that exposure to Cd can cause disturbances in the lipid metabolism, amino acid metabolism, nervous system, antioxidant defence system, liver and kidney function.

A plasma metabolomic signature discloses human breast cancer

PURPOSE

Metabolomics is the comprehensive global study of metabolites in biological samples. In this retrospective pilot study we explored whether serum metabolomic profile can discriminate the presence of human breast cancer irrespective of the cancer subtype.

METHODS

Plasma samples were analyzed from healthy women (n = 20) and patients with breast cancer after diagnosis (n = 91) using a liquid chromatography-mass spectrometry platform. Multivariate statistics and a Random Forest (RF) classifier were used to create a metabolomics panel for the diagnosis of human breast cancer.

RESULTS

Metabolomics correctly distinguished between breast cancer patients and healthy control subjects. In the RF supervised class prediction analysis comparing breast cancer and healthy control groups, RF accurately classified 100% both samples of the breast cancer patients and healthy controls. So, the class error for both group in and the out-of-bag error were 0. We also found 1269 metabolites with different concentration in plasma from healthy controls and cancer patients; and basing on exact mass, retention time and isotopic distribution we identified 35 metabolites. These metabolites mostly support cell growth by providing energy and building stones for the synthesis of essential biomolecules, and function as signal transduction molecules. The collective results of RF, significance testing, and false discovery rate analysis identified several metabolites that were strongly associated with breast cancer.

CONCLUSIONS

In breast cancer a metabolomics signature of cancer exists and can be detected in patient plasma irrespectively of the breast cancer type.

Primary Metabolism and Medium-Chain Fatty Acid Alterations Precede Long-Chain Fatty Acid Changes Impacting Neutral Lipid Metabolism in Response to an Anticancer Lysophosphatidylcholine Analogue in Yeast

The nonmetabolizable lysophosphatidylcholine (LysoPC) analogue edelfosine is the prototype of a class of compounds being investigated for their potential as selective chemotherapeutic agents. Edelfosine targets membranes, disturbing cellular homeostasis. Is not clear at this point how membrane alterations are communicated between intracellular compartments leading to growth inhibition and eventual cell death. In the present study, a combined metabolomics/lipidomics approach for the unbiased identification of metabolic pathways altered in yeast treated with sublethal concentrations of the LysoPC analogue was employed. Mass spectrometry of polar metabolites, fatty acids, and lipidomic profiling was used to study the effects of edelfosine on yeast metabolism. Amino acid and sugar metabolism, the Krebs cycle, and fatty acid profiles were most disrupted, with polar metabolites and short-medium chain fatty acid changes preceding long and very long-chain fatty acid variations. Initial increases in metabolites such as trehalose, proline, and γ-amino butyric acid with a concomitant decrease in metabolites of the Krebs cycle, citrate and fumarate, are interpreted as a cellular attempt to offset oxidative stress in response to mitochondrial dysfunction induced by the treatment. Notably, alanine, inositol, and myristoleic acid showed a steady increase during the period analyzed (2, 4, and 6 h after treatment). Of importance was the finding that edelfosine induced significant alterations in neutral glycerolipid metabolism resulting in a significant increase in the signaling lipid diacylglycerol.

Optimized Method for Untargeted Metabolomics Analysis of MDA-MB-231 Breast Cancer Cells

Cancer cells often have dysregulated metabolism, which is largely characterized by the Warburg effect-an increase in glycolytic activity at the expense of oxidative phosphorylation-and increased glutamine utilization. Modern metabolomics tools offer an efficient means to investigate metabolism in cancer cells. Currently, a number of protocols have been described for harvesting adherent cells for metabolomics analysis, but the techniques vary greatly and they lack specificity to particular cancer cell lines with diverse metabolic and structural features. Here we present an optimized method for untargeted metabolomics characterization of MDA-MB-231 triple negative breast cancer cells, which are commonly used to study metastatic breast cancer. We found that an approach that extracted all metabolites in a single step within the culture dish optimally detected both polar and non-polar metabolite classes with higher relative abundance than methods that involved removal of cells from the dish. We show that this method is highly suited to diverse applications, including the characterization of central metabolic flux by stable isotope labelling and differential analysis of cells subjected to specific pharmacological interventions.

HILIC-MS for metabolomics: An attractive and complementary approach to RPLC-MS

Hydrophilic interaction chromatography (HILIC) is an emerging separation mode of liquid chromatography (LC). Using highly hydrophilic stationary phases capable of retaining polar/ionic metabolites, and accompany with high organic content mobile phase that offer readily compatibility with mass spectrometry (MS) has made HILIC an attractive complementary tool to the widely used reverse-phase (RP) chromatographic separations in metabolomic studies. The combination of HILIC and RPLC coupled with an MS detector expands the number of detected analytes and provides more comprehensive metabolite coverage than use of only RP chromatography. This review describes the recent applications of HILIC-MS/MS in metabolomic studies, ranging from amino acids, lipids, nucleotides, organic acids, pharmaceuticals, and metabolites of specific nature. The biological systems investigated include microbials, cultured cell line, plants, herbal medicine, urine, and serum as well as tissues from animals and humans. Owing to its unique capability to measure more-polar biomolecules, the HILIC separation technique would no doubt enhance the comprehensiveness of metabolite detection, and add significant value for metabolomic investigations. © 2014 Wiley Periodicals, Inc. Mass Spec Rev 35:574-600, 2016.

Utilization of metabonomics to identify serum biomarkers in murine H22 hepatocarcinoma and deduce antitumor mechanism of Rhizoma Paridis saponins

Murine H22 hepatocarcinoma model is so popular to be used for the preclinical anticancer candidate's evaluation. However, the metabolic biomarkers of this model were not identified. Meanwhile, Rhizoma Paridis saponins (RPS) as natural products have been found to show strong antitumor activity, while its anti-cancer mechanism is not clear. To search for potential metabolite biomarkers of this model, serum metabonomics approach was applied to detect the variation of metabolite biomarkers and the related metabolism genes and signaling pathway were used to deduce the antitumor mechanisms of RPS. As a result, ten serum metabolites were identified in twenty-four mice including healthy mice, non-treated cancer mice, RPS-treated cancer mice and RPS-treated healthy mice. RPS significantly decreased tumor weight correlates to down-regulating lactate, acetate, N-acetyl amino acid and glutamine signals (p < 0.05), which were marked metabolites screened according to the very important person (VIP), loading plot and receiver operating characteristic curve (ROC) tests. For the analysis of metabolic enzyme related genes, RPS reversed the aerobic glycolysis through activating tumor suppressor p53 and PTEN, and suppressed FASN to inhibit lipogenesis. What's more, RPS repressed Myc and GLS expression and decreased glutamine level. The regulating PI3K/Akt/mTOR and HIF-1α/Myc/Ras networks also participated in these metabolic changes. Taken together, RPS suppressed ATP product made the tumor growth slow, which indicated a good anti-cancer effect and new angle for understanding the mechanism of RPS. In conclusion, this study demonstrated that the utility of (1)H NMR metabolic profiles taken together with tumor weight and viscera index was a promising screening tool for evaluating the antitumor effect of candidates. In addition, RPS was a potent anticancer agent through inhibiting cancer cellular metabolism to suppress proliferation in hepatoma H22 tumor murine, which promoted the application of RPS in the future.

A Pilot Study on the Utility of Serum Metabolomics in Neuroblastoma Patients and Xenograft Models

BACKGROUND

Improved prediction of neuroblastoma (NB) behavior is needed to detect treatment-refractory disease and may allow further reduction in therapy for some patients. In this regard, serum metabolomic analysis has proven utility in several cancer types. We hypothesize that serum metabolomic analysis will correlate with risk-group classification for patients with NB, and sensitively detect NB in murine xenograft models.

PROCEDURE

A pilot study was done on Children's Oncology Group (COG) tumor bank sera from 10 patients (five high-, five low-risk). An institutional pilot study was carried out on five patients comparing sera obtained during active versus minimal disease (complete response/very good partial response; CR/VGPR).

XENOGRAFT

Flank tumors were established in Nu/Nu mice by injection of NB cell lines (IMR-32, SH-EP, SK-N-AS). Serum for comparison was drawn pre-injection, at 1 week after injection when there was no visible tumor, and again once tumors were grossly visible. Comparisons were also made between tumor bearing mouse serum and supernatants from NB cell lines.

METABOLOMIC ANALYSIS

Samples were analyzed by nuclear magnetic resonance and/or gas chromatography-mass spectrometry. Multivariate data analysis was conducted using SIMCA-P (Umetrics).

RESULTS

Serum metabolomic analysis differentiated high- and low-risk patients as well as active disease from CR/VGPR. Differences were in nitrogen, amino acid, and carbohydrate metabolism, as well as ketosis. The serum metabolomic signature in murine xenograft models sensitively detected NB cells and correlated with disease burden. Similar metabolic changes attributable to NB were noted in both human and murine serum.

CONCLUSIONS

Serum metabolomic analysis can distinguish several characteristics of NB. A larger analysis of COG banked sera is warranted.

Translational metabolomics in cancer research

Over the last decade there has been a bottleneck in the introduction of new validated cancer metabolic biomarkers into clinical practice. Unfortunately, there are no biomarkers with adequate sensitivity for the early detection of cancer, and there remain a reliance on cancer antigens for monitoring treatment. The need for new diagnostics has led to the exploration of untargeted metabolomics for discovery of early biomarkers of specific cancers and targeted metabolomics to elucidate mechanistic aspects of tumor progression. The successful translation of such strategies to the treatment of cancer would allow earlier intervention to improve survival. We have reviewed the methodology that is being used to achieve these goals together with recent advances in implementing translational metabolomics in cancer.

Influence of glutathione-S-transferase (GST) inhibition on lung epithelial cell injury: role of oxidative stress and metabolism

Oxidant-mediated tissue injury is key to the pathogenesis of acute lung injury. Glutathione- S-transferases (GSTs) are important detoxifying enzymes that catalyze the conjugation of glutathione with toxic oxidant compounds and are associated with acute and chronic inflammatory lung diseases. We hypothesized that attenuation of cellular GST enzymes would augment intracellular oxidative and metabolic stress and induce lung cell injury. Treatment of murine lung epithelial cells with GST inhibitors, ethacrynic acid (EA), and caffeic acid compromised lung epithelial cell viability in a concentration-dependent manner. These inhibitors also potentiated cell injury induced by hydrogen peroxide (H2O2), tert-butyl-hydroperoxide, and hypoxia and reoxygenation (HR). SiRNA-mediated attenuation of GST-π but not GST-μ expression reduced cell viability and significantly enhanced stress (H2O2/HR)-induced injury. GST inhibitors also induced intracellular oxidative stress (measured by dihydrorhodamine 123 and dichlorofluorescein fluorescence), caused alterations in overall intracellular redox status (as evidenced by NAD+/NADH ratios), and increased protein carbonyl formation. Furthermore, the antioxidant N-acetylcysteine completely prevented EA-induced oxidative stress and cytotoxicity. Whereas EA had no effect on mitochondrial energetics, it significantly altered cellular metabolic profile. To explore the physiological impact of these cellular events, we used an ex vivo mouse-isolated perfused lung model. Supplementation of perfusate with EA markedly affected lung mechanics and significantly increased lung permeability. The results of our combined genetic, pharmacological, and metabolic studies on multiple platforms suggest the importance of GST enzymes, specifically GST-π, in the cellular and whole lung response to acute oxidative and metabolic stress. These may have important clinical implications.

Investigation of salivary free amino acid profile for early diagnosis of breast cancer with ultra performance liquid chromatography-mass spectrometry

BACKGROUND

Breast cancer (BC) is the second leading cause of cancer death in women worldwide. This study aimed at investigating salivary free amino acid (SFAA) profile to facilitate the early diagnosis of BC.

METHODS

Unstimulated whole saliva from BC patients (n=27) and randomly from healthy females (n=28) were assayed with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance of each of the amino acid (AA) biomarkers, or SFAA profile index for BC screening.

RESULTS

Concentrations of 15 SFAAs demonstrated significant differences (P<0.05) between BC patients at stages I-II and healthy controls (HC). The area under the curve (AUC) values in HC vs BCs I-II based on single AA were from 0.695 to 0.866. The AUC for SFAA profile index combined Pro, Thr, His was 0.916 (sensitivity 88.2%, specificity 85.7%) in distinguishing HC from BCs I-II.

CONCLUSIONS

The diagnostic potentials of 15 SFAAs as early diagnostic biomarkers for BC were verified and the diagnostic accuracy was improved in the use of SFAA profile index. The detection of SFAA profile is expected to be applied for the preclinical screening of early stage of BC in the future.

Application of metabolomics in drug resistant breast cancer research

The metabolic profiles of breast cancer cells are different from normal mammary epithelial cells. Breast cancer cells that gain resistance to therapeutic interventions can reprogram their endogenous metabolism in order to adapt and proliferate despite high oxidative stress and hypoxic conditions. Drug resistance in breast cancer, regardless of subgroups, is a major clinical setback. Although recent advances in genomics and proteomics research has given us a glimpse into the heterogeneity that exists even within subgroups, the ability to precisely predict a tumor’s response to therapy remains elusive. Metabolomics as a quantitative, high through put technology offers promise towards devising new strategies to establish predictive, diagnostic and prognostic markers of breast cancer. Along with other “omics” technologies that include genomics, transcriptomics, and proteomics, metabolomics fits into the puzzle of a comprehensive systems biology approach to understand drug resistance in breast cancer. In this review, we highlight the challenges facing successful therapeutic treatment of breast cancer and the innovative approaches that metabolomics offers to better understand drug resistance in cancer.

Genotype-specific differences in the tumor metabolite profile of pheochromocytoma and paraganglioma using untargeted and targeted metabolomics

CONTEXT AND OBJECTIVE

Pheochromocytomas and paragangliomas (PGLs) are neuroendocrine tumors of sympathetic or parasympathetic paraganglia. Nearly 40% of PGLs are caused by germline mutations. The present study investigated the effect of genetic alterations on metabolic networks in PGLs.

DESIGN

Homogenates of 32 sporadic PGLs and 48 PGLs from patients with mutations in SDHB, SDHD, SDHAF-2, VHL, RET, and NF-1 were subjected to proton ((1)H) nuclear magnetic resonance (NMR) spectroscopy at 500 MHz for untargeted and HPLC tandem mass spectrometry for targeted metabolite profiling.

RESULTS

(1)H NMR spectroscopy identified 28 metabolites in PGLs of which 12 showed genotype-specific differences. Part of these results published earlier reported low complex II activity (P < .0001) and low ATP/ADP/AMP content (P < .001) in SDH-related PGLs compared with sporadics and PGLs of other genotypes. Extending these results, low levels of N-acetylaspartic acid (NAA; P < .05) in SDH tumors and creatine (P < .05) in VHL tumors were observed compared with sporadics and other genotypes. Positive correlation was observed between NAA and ATP/ADP/AMP content (P < .001) and NAA and complex II activity (P < .0001) of PGLs. Targeted purine analysis in PGLs showed low adenine in cluster 1 compared with cluster 2 tumors (SDH P < .0001; VHL P < .05) whereas lower levels (P < .05) of guanosine and hypoxanthine were observed in RET tumors compared with SDH tumors. Principal component analysis (PCA) of metabolites could distinguish PGLs of different genotypes.

CONCLUSIONS

The present study gives a comprehensive picture of alterations in energy metabolism in SDH- and VHL-related PGLs and establishes the interrelationship of energy metabolism and amino acid and purine metabolism in PGLs.

Analysis of plasma metabolic biomarkers in the development of 4-nitroquinoline-1-oxide-induced oral carcinogenesis in rats

The aim of the present study was to identify time-dependent changes in the expression of metabolic biomarkers during the various stages of oral carcinogenesis to provide an insight into the sequential mechanism of oral cancer development. An ¹H nuclear magnetic resonance (NMR)-based metabolomics approach was used to analyze the blood plasma samples of Sprague-Dawley rats exhibiting various oral lesions induced by the administration of 4-nitroquinoline-1-oxide (4NQO) in drinking water. The ¹H NMR spectra were processed by principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) to determine the metabolic differences between the three developmental stages of oral mucosa cancer (health, oral leukoplakia [OLK] and oral squamous cell carcinoma [OSCC]). The variable importance in projection (VIP) score derived from the PLS-DA model was used to screen for important metabolites, whose significance was further verified through analysis of variance (ANOVA). Data from the present study indicated that 4NQO-induced rat oral carcinogenesis produced oral pre-neoplastic and neoplastic lesions and provided an effective model for analyzing sequential changes in the ¹H NMR spectra of rat blood plasma. The ¹H NMR-based metabolomics approach clearly differentiates between healthy, OLK and OSSC rats in the PCA and PLS-DA models. Furthermore, lactic acid, choline, glucose, proline, valine, isoleucine, aspartic acid and 2-hydroxybutyric acid demonstrated VIP>1 in the PLS-D model and P<0.05 with ANOVA. It was also identified that increases in lactic acid, choline and glucose, and decreases in proline, valine, isoleucine, aspartic acid and 2-hydroxybutyric acid may be relative to the characteristic mechanisms of oral carcinogenesis. Therefore, these plasma metabolites may serve as metabolic biomarkers in oral carcinogenesis and assist in the early diagnosis and preventive treatment of oral cancer.

Metabolomics in cell culture--a strategy to study crucial metabolic pathways in cancer development and the response to treatment

Metabolomics is a comprehensive tool for monitoring processes within biological systems. Thus, metabolomics may be widely applied to the determination of diagnostic biomarkers for certain diseases or treatment outcomes. There is significant potential for metabolomics to be implemented in cancer research because cancer may modify metabolic pathways in the whole organism. However, not all biological questions can be answered solely by the examination of small molecule composition in biofluids; in particular, the study of cellular processes or preclinical drug testing requires ex vivo models. The major objective of this review was to summarise the current achievement in the field of metabolomics in cancer cell culture-focusing on the metabolic pathways regulated in different cancer cell lines-and progress that has been made in the area of drug screening and development by the implementation of metabolomics in cell lines.

IDH mutations in tumorigenesis and their potential role as novel therapeutic targets

Isocitrate dehydrogenases (IDHs) catalyze the oxidative decarboxylation of isocitrate to α-ketoglutarate (α-KG). Somatic mutations in genes encoding IDH1 and IDH2 were first identified in glioma and subsequently in acute myeloid leukemia and other solid tumors. These heterozygous point mutations occur at the arginine residue of the enzyme's active site and cause both loss of normal enzyme function and gain of function, causing reduction of α-KG to D-2-hydroxyglutarate, which accumulates. D-2-hydroxyglutarate may act as an oncometabolite through the inhibition of various α-KG-dependent enzymes, stimulating angiogenesis, histone modifications and aberrant DNA methylation. Possibly, IDH mutations may also cause oncogenic effects through dysregulation of the tricarboxylic acid cycle, or by increasing susceptibility to oxidative stress. Clinically, IDH mutations may be useful diagnostic, prognostic and predictive biomarkers, and it is anticipated that a better understanding of the pathogenesis of IDH mutations will enable IDH-directed therapies to be developed in the future.

¹H-NMR protocol for exometabolome analysis of cultured mammalian cells

(1)H-Nuclear magnetic resonance ((1)H-NMR) spectroscopy is a powerful technique to analyze the composition of complex mixtures based on the particular proton fingerprint of each molecule. Here we describe a protocol for exometabolome analysis of mammalian cells using this technique, including sample preparation, spectra acquisition, and integration. The potential of this technique is exemplified by application to cultures of a Chinese hamster ovary (CHO) cell line. The average error associated to this method is under 3% and the limit of quantification for all metabolites analyzed is below 180 μM.

Republished study: long-term toxicity of a Roundup herbicide and a Roundup-tolerant genetically modified maize

BACKGROUND

The health effects of a Roundup-tolerant NK603 genetically modified (GM) maize (from 11% in the diet), cultivated with or without Roundup application and Roundup alone (from 0.1 ppb of the full pesticide containing glyphosate and adjuvants) in drinking water, were evaluated for 2 years in rats. This study constitutes a follow-up investigation of a 90-day feeding study conducted by Monsanto in order to obtain commercial release of this GMO, employing the same rat strain and analyzing biochemical parameters on the same number of animals per group as our investigation. Our research represents the first chronic study on these substances, in which all observations including tumors are reported chronologically. Thus, it was not designed as a carcinogenicity study. We report the major findings with 34 organs observed and 56 parameters analyzed at 11 time points for most organs.

RESULTS

Biochemical analyses confirmed very significant chronic kidney deficiencies, for all treatments and both sexes; 76% of the altered parameters were kidney-related. In treated males, liver congestions and necrosis were 2.5 to 5.5 times higher. Marked and severe nephropathies were also generally 1.3 to 2.3 times greater. In females, all treatment groups showed a two- to threefold increase in mortality, and deaths were earlier. This difference was also evident in three male groups fed with GM maize. All results were hormone- and sex-dependent, and the pathological profiles were comparable. Females developed large mammary tumors more frequently and before controls; the pituitary was the second most disabled organ; the sex hormonal balance was modified by consumption of GM maize and Roundup treatments. Males presented up to four times more large palpable tumors starting 600 days earlier than in the control group, in which only one tumor was noted. These results may be explained by not only the non-linear endocrine-disrupting effects of Roundup but also by the overexpression of the EPSPS transgene or other mutational effects in the GM maize and their metabolic consequences.

CONCLUSION

Our findings imply that long-term (2 year) feeding trials need to be conducted to thoroughly evaluate the safety of GM foods and pesticides in their full commercial formulations.

The function of the respiratory supercomplexes: the plasticity model

Mitochondria are important organelles not only as efficient ATP generators but also in controlling and regulating many cellular processes. Mitochondria are dynamic compartments that rearrange under stress response and changes in food availability or oxygen concentrations. The mitochondrial electron transport chain parallels these rearrangements to achieve an optimum performance and therefore requires a plastic organization within the inner mitochondrial membrane. This consists in a balanced distribution between free respiratory complexes and supercomplexes. The mechanisms by which the distribution and organization of supercomplexes can be adjusted to the needs of the cells are still poorly understood. The aim of this review is to focus on the functional role of the respiratory supercomplexes and its relevance in physiology. This article is part of a Special Issue entitled: Dynamic and ultrastructure of bioenergetic membranes and their components.

Metabolomic analysis of anti-hypoxia and anti-anxiety effects of Fu Fang Jin Jing Oral Liquid

Background

Herba Rhodiolae is a traditional Chinese medicine used by the Tibetan people for treating hypoxia related diseases such as anxiety. Based on the previous work, we developed and patented an anti-anxiety herbal formula Fu Fang Jin Jing Oral Liquid (FJJOL) with Herba Rhodiolae as a chief ingredient. In this study, the anti-hypoxia and anti-anxiety effects of FJJOL in a high altitude forced-swimming mouse model with anxiety symptoms will be elucidated by NMR-based metabolomics.

Methods

In our experiments, the mice were divided randomly into four groups as flatland group, high altitude saline-treated group, high altitude FJJOL-treated group, and high altitude diazepam-treated group. To cause anxiety effects and hypoxic defects, a combination use of oxygen level decreasing (hypobaric cabin) and oxygen consumption increasing (exhaustive swimming) were applied to mice. After a three-day experimental handling, aqueous metabolites of mouse brain tissues were extracted and then subjected to NMR analysis. The therapeutic effects of FJJOL on the hypobaric hypoxia mice with anxiety symptoms were verified.

Results

Upon hypoxic exposure, both energy metabolism defects and disorders of functional metabolites in brain tissues of mice were observed. PCA, PLS-DA and OPLS-DA scatter plots revealed a clear group clustering for metabolic profiles in the hypoxia versus normoxia samples. After a three-day treatment with FJJOL, significant rescue effects on energy metabolism were detected, and levels of ATP, fumarate, malate and lactate in brain tissues of hypoxic mice recovered. Meanwhile, FJJOL also up-regulated the neurotransmitter GABA, and the improvement of anxiety symptoms was highly related to this effect.

Conclusions

FJJOL ameliorated hypobaric hypoxia effects by regulating energy metabolism, choline metabolism, and improving the symptoms of anxiety. The anti-anxiety therapeutic effects of FJJOL were comparable to the conventional anti-anxiety drug diazepam on the hypobaric hypoxia mice. FJJOL might serve as an alternative therapy for the hypoxia and anxiety disorders.

Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer

Breast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long-term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective treatments. In this study, serum metabolite profiling was performed to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy for breast cancer. Metabolic profiles of serum from patients with complete (n = 8), partial (n = 14) and no response (n = 6) to chemotherapy were studied using a combination of nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography-mass spectrometry (LC-MS) and statistical analysis methods. The concentrations of four metabolites, three (threonine, isoleucine, glutamine) from NMR and one (linolenic acid) from LC-MS were significantly different when comparing response to chemotherapy. A prediction model developed by combining NMR and MS derived metabolites correctly identified 80% of the patients whose tumors did not show complete response to chemotherapy. These results show promise for larger studies that could result in more personalized treatment protocols for breast cancer patients.

The future of NMR metabolomics in cancer therapy: towards personalizing treatment and developing targeted drugs?

There has been a recent shift in how cancers are defined, where tumors are no longer simply classified by their tissue origin, but also by their molecular characteristics. Furthermore, personalized medicine has become a popular term and it could start to play an important role in future medical care. However, today, a "one size fits all" approach is still the most common form of cancer treatment. In this mini-review paper, we report on the role of nuclear magnetic resonance (NMR) metabolomics in drug development and in personalized medicine. NMR spectroscopy has successfully been used to evaluate current and potential therapies, both single-agents and combination therapies, to analyze toxicology, optimal dose, resistance, sensitivity, and biological mechanisms. It can also provide biological insight on tumor subtypes and their different responses to drugs, and indicate which patients are most likely to experience off-target effects and predict characteristics for treatment efficacy. Identifying pre-treatment metabolic profiles that correlate to these events could significantly improve how we view and treat tumors. We also briefly discuss several targeted cancer drugs that have been studied by metabolomics. We conclude that NMR technology provides a key platform in metabolomics that is well-positioned to play a crucial role in realizing the ultimate goal of better tailored cancer medicine.

Metabolomics in noninvasive breast cancer

Breast cancer remains the most leading cause of death among women worldwide. Common methods for diagnosis and surveillance include mammography, histopathology and blood tests. The major drawback of mammography is the high rate of false reports, aside from the risk from repeated exposure to harmful ionizing radiations; histopathology is time consuming and often prone to subjective interpretations; blood-based tests are attractive, but lack the sensitivity and specificity. Obviously, more sensitive biomarkers for early detection and molecular targets for better treating breast cancer are urgently needed. Fortunately, molecular level 'omics' diagnosis is becoming increasingly popular; metabolomics, diagnosis based on 'metabolic fingerprinting' may provide clinically useful biomarkers applied toward identifying metabolic alterations and has introduced new insights into the pathology of breast cancer. By applying advanced analytical and statistical tools, metabolomics involves the comprehensive profiling of the full complement of low molecular weight compounds in a biological system and could classify the basis of tumor biology of breast cancer, to identify new prognostic and predictive markers and discover new targets for future therapeutic interventions. This advanced bioanalytic methods may now open new avenues for diagnostics in cancer via discovery of biomarkers. In this review we take a closer look at the metabolomics used within the field of breast cancer diagnosis. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples.

Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data

Hypoxia affects the tumor microenvironment and is considered important to metastasis progression and therapy resistance. Thus far, the majority of global analyses of tumor hypoxia responses have been limited to just a single omics level. Combining multiple omics data can broaden our understanding of tumor hypoxia. Here, we investigate the temporal change of the metabolite composition with gene expression data from literature to provide a more comprehensive insight into the system level in response to hypoxia. Nuclear magnetic resonance spectroscopy was used to perform metabolomic profiling on the MDA-MB-231 breast cancer cell line under hypoxic conditions. Multivariate statistical analysis revealed that the metabolic difference between hypoxia and normoxia was similar over 24 h, but became distinct over 48 h. Time dependent microarray data from the same cell line in the literature displayed different gene expressions under hypoxic and normoxic conditions mostly at 12 h or earlier. The direct metabolomic profiles show a large overlap with theoretical metabolic profiles deduced from previous transcriptomic studies. Consistent pathways are glycolysis/gluconeogenesis, pyruvate, purine and arginine and proline metabolism. Ten metabolic pathways revealed by metabolomics were not covered by the downstream of the known transcriptomic profiles, suggesting new metabolic phenotypes. These results confirm previous transcriptomics understanding and expand the knowledge from existing models on correlation and co-regulation between transcriptomic and metabolomics profiles, which demonstrates the power of integrated omics analysis.

Understanding the hypoxic niche of multiple myeloma: therapeutic implications and contributions of mouse models

Multiple myeloma (MM) is the second most common hematological malignancy and is characterized by the clonal expansion of plasma cells in the bone marrow. Recently, hypoxia has received increased interest in the context of MM, in both basic and translational research. In this review, we describe the discovery of the hypoxic niche in MM and how it can be targeted therapeutically. We also discuss mouse models that closely mimic human MM, highlighting those that allow preclinical research into new therapies that exploit the hypoxic niche in MM.

Plurality of opinion, scientific discourse and pseudoscience: an in depth analysis of the Séralini et al. study claiming that Roundup™ Ready corn or the herbicide Roundup™ cause cancer in rats

A recent paper published in the journal Food and Chemical Toxicology presents the results of a long-term toxicity study related to a widely-used commercial herbicide (Roundup™) and a Roundup-tolerant genetically modified variety of maize, concluding that both the herbicide and the maize varieties are toxic. Here we discuss the many errors and inaccuracies in the published article resulting in highly misleading conclusions, whose publication in the scientific literature and in the wider media has caused damage to the credibility of science and researchers in the field. We and many others have criticized the study, and in particular the manner in which the experiments were planned, implemented, analyzed, interpreted and communicated. The study appeared to sweep aside all known benchmarks of scientific good practice and, more importantly, to ignore the minimal standards of scientific and ethical conduct in particular concerning the humane treatment of experimental animals.

Long term toxicity of a Roundup herbicide and a Roundup-tolerant genetically modified maize

The health effects of a Roundup-tolerant genetically modified maize (from 11% in the diet), cultivated with or without Roundup, and Roundup alone (from 0.1 ppb in water), were studied 2 years in rats. In females, all treated groups died 2-3 times more than controls, and more rapidly. This difference was visible in 3 male groups fed GMOs. All results were hormone and sex dependent, and the pathological profiles were comparable. Females developed large mammary tumors almost always more often than and before controls, the pituitary was the second most disabled organ; the sex hormonal balance was modified by GMO and Roundup treatments. In treated males, liver congestions and necrosis were 2.5-5.5 times higher. This pathology was confirmed by optic and transmission electron microscopy. Marked and severe kidney nephropathies were also generally 1.3-2.3 greater. Males presented 4 times more large palpable tumors than controls which occurred up to 600 days earlier. Biochemistry data confirmed very significant kidney chronic deficiencies; for all treatments and both sexes, 76% of the altered parameters were kidney related. These results can be explained by the non linear endocrine-disrupting effects of Roundup, but also by the overexpression of the transgene in the GMO and its metabolic consequences.

Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis

Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.

Ratio analysis nuclear magnetic resonance spectroscopy for selective metabolite identification in complex samples

Metabolite identification in the complex NMR spectra of biological samples is a challenging task due to significant spectral overlap and limited signal-to-noise. In this study we present a new approach, RANSY (ratio analysis NMR spectroscopy), which identifies all the peaks of a specific metabolite on the basis of the ratios of peak heights or integrals. We show that the spectrum for an individual metabolite can be generated by exploiting the fact that the peak ratios for any metabolite in the NMR spectrum are fixed and proportional to the relative numbers of magnetically distinct protons. When the peak ratios are divided by their coefficients of variation derived from a set of NMR spectra, the generation of an individual metabolite spectrum is enabled. We first tested the performance of this approach using one-dimensional (1D) and two-dimensional (2D) NMR data of mixtures of synthetic analogues of common body fluid metabolites. Subsequently, the method was applied to (1)H NMR spectra of blood serum samples to demonstrate the selective identification of a number of metabolites. The RANSY approach, which does not need any additional NMR experiments for spectral simplification, is easy to perform and has the potential to aid in the identification of unknown metabolites using 1D or 2D NMR spectra in virtually any complex biological mixture.

siRNA-mediated knockdown of aryl hydrocarbon receptor nuclear translocator 2 affects hypoxia-inducible factor-1 regulatory signaling and metabolism in human breast cancer cells

Recent human studies found that the mRNA expression level of aryl-hydrocarbon receptor nuclear translocator 2 (ARNT2) was positively associated with the prognosis of breast cancer. In this study, we used small interfering RNA techniques to knockdown ARNT2 expression in MCF7 human breast cancer cells, and found that an almost 40% downregulation of ARNT2 mRNA expression increased the expression of sensitive to apoptosis gene (3.36-fold), and decreased the expression of von Hippel-Lindau (0.27-fold) and matrix metalloproteinase-1 (0.35-fold). The metabolite analysis revealed the contents of glucose, glycine, betaine, phosphocholine, pyruvate and lactate involved in the hypoxia-inducible factor (HIF)-1-dependent glycolytic pathway were significantly lower in cells treated with siARNT2. Our results suggested that ARNT2 might play an important role in the modulation of HIF-1-regulated signaling and metabolism.